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Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation ...
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Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation
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In: Transactions of the Association for Computational Linguistics, 7, 313–325 ; ISSN: 2307-387X (2022)
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MasakhaNER: Named entity recognition for African languages
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In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021)
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Measuring and Increasing Context Usage in Context-Aware Machine Translation ...
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Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas ...
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Explorations in Transfer Learning for OCR Post-Correction ...
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Detecting Hallucinated Content in Conditional Neural Sequence Generation ...
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CitationIE: Leveraging the Citation Graph for Scientific Information Extraction ...
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Efficient Test Time Adapter Ensembling for Low-resource Language Varieties ...
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Explicit Alignment Objectives for Multilingual Bidirectional Encoders ...
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Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models ...
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Distributionally Robust Multilingual Machine Translation ...
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Multi-view Subword Regularization ...
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Abstract:
Read the paper on the folowing link: https://www.aclweb.org/anthology/2021.naacl-main.40/ Abstract: Multilingual pretrained representations generally rely on subword segmentation algorithms to create a shared multilingual vocabulary. However, standard heuristic algorithms often lead to sub-optimal segmentation, especially for languages with limited amounts of data. In this paper, we take two major steps towards alleviating this problem. First, we demonstrate empirically that applying existing subword regularization methods (Kudo, 2018;Provilkov et al., 2020) during fine-tuning of pre-trained multilingual representations improves the effectiveness of cross-lingual transfer. Second, to take full advantage of different possible input segmentations, we propose Multi-view Subword Regularization (MVR), a method that enforces the consistency between predictions of using inputs tokenized by the standard and probabilistic segmentations. Results on the XTREME multilingual benchmark (Hu et al., 2020) show that MVR ...
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Keyword:
Artificial Intelligence; Computer Science and Engineering; Intelligent System; Natural Language Processing
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URL: https://underline.io/lecture/19832-multi-view-subword-regularization https://dx.doi.org/10.48448/y8tg-sw51
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When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection ...
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Lexically-Aware Semi-Supervised Learning for OCR Post-Correction ...
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Phrase-level Active Learning for Neural Machine Translation ...
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Do Context-Aware Translation Models Pay the Right Attention? ...
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